Spatial Analysis training is available as "onsite live training" or "remote live training". Onsite live Spatial Analysis training can be carried out locally on customer premises in Denmark or in NobleProg corporate training centers in Denmark. Remote live training is carried out by way of an interactive, remote desktop.

NobleProg -- Your Local Training Provider

Testimonials

★★★★★

★★★★★

Very clear and information packed course, would definitely recommend it.

Elsa Mitchell - Babcock International

Course: Excel For Statistical Data Analysis

Just wish to reiterate that Muhammad appeared to be a very motivated trainer who proved to be extremely knowledgeable and passionate about his subject.

Aurel Kessler - ECB

Course: Excel For Statistical Data Analysis

Very clear and information packed course, would definitely recommend it.

Elsa Mitchell - Babcock International

Course: Excel For Statistical Data Analysis

A lot of exercises, good explanation.

Katarzyna Rompala - UBS Kraków Sp. z o.o.

Course: Excel For Statistical Data Analysis

What did you like the most about the training?:
Trainer attitude and knowledge.

Maria Ożóg - Stefko - Amway Business Center Europe

Course: Excel For Statistical Data Analysis

What did you like the most about the training?:
How easily he explains and all the exercises.

JOanna Tomzik - Amway Business Center Europe

Course: Excel For Statistical Data Analysis

What did you like the most about the training?:
A lot of examples and exercises.

Agnieszka Krasuska - Citibank Europe PLC

Course: Excel For Statistical Data Analysis

What did you like the most about the training?:
The examples given for each excel function and also many useful shortcuts.

Izabela Niziołek - Citibank Europe PLC

Course: Excel For Statistical Data Analysis

I get answers on all my questions.

Natalia Gladii

Course: Data Analytics With R

I really was benefit from the willingness of the trainer to share more.

Balaram Chandra Paul

Course: A Practical Introduction to Data Analysis and Big Data

Liked very much the interactive way of learning.

Luigi Loiacono

Course: Data Analysis with Hive/HiveQL

I enjoyed the Excel sheets provided having the exercises with examples. This meant that if Tamil was held up helping other people, I could crack on with the next parts.

Luke Pontin

Course: Data and Analytics - from the ground up

Learning how to use excel properly.

Torin Mitchell

Course: Data and Analytics - from the ground up

The way the trainer made complex subjects easy to understand.

Adam Drewry

Course: Data and Analytics - from the ground up

It was a very practical training, I liked the hands-on exercises.

Proximus

Course: Data Analysis with Hive/HiveQL

Detailed and comprehensive instruction given by experienced and clearly knowledgeable expert on the subject.

Justin Roche

Course: Data and Analytics - from the ground up

Tamil is very knowledgeable and nice person, I have learned from him a lot.

Aleksandra Szubert

Course: Data and Analytics - from the ground up

I liked the first session. Very intensive and quick.

Digital Jersey

Course: Data and Analytics - from the ground up

I was benefit from the good overview, good balance between theory and exercises.

Proximus

Course: Data Analysis with Hive/HiveQL

I mostly liked the patience of Tamil.

Laszlo Maros

Course: Data and Analytics - from the ground up

I enjoyed the dynamic interaction and “hands-on” the subject, thanks to the Virtual Machine, very stimulating!.

Philippe Job

Course: Data Analysis with Hive/HiveQL

I really was benefit from the real life practical examples.

Wioleta (Vicky) Celinska-Drozd

Course: Data and Analytics - from the ground up

I was benefit from the competence and knowledge of the trainer.

Jonathan Puvilland

Course: Data Analysis with Hive/HiveQL

It covered a broad range of information.

Continental AG / Abteilung: CF IT Finance

Course: A Practical Introduction to Data Analysis and Big Data

I generally was benefit from the presentation of technologies.

Continental AG / Abteilung: CF IT Finance

Course: A Practical Introduction to Data Analysis and Big Data

Overall the Content was good.

Sameer Rohadia

Course: A Practical Introduction to Data Analysis and Big Data

I liked the customized, in-house file processing and data analysis.

Glycom A/S

Course: Data Analysis in Python using Pandas and Numpy

I enjoyed the that we have used our own data as examples.

Glycom A/S

Course: Data Analysis in Python using Pandas and Numpy

I really liked the exercises on time series modeling.

Teleperformance

Course: Data Analytics With R

New tool which is “R” and I find it interesting to know the existence of such tool for data analysis.

Michael Lopez - Teleperformance

Course: Data Analytics With R

The tool was interesting and I see the use. I would like to learn about more about it.

Teleperformance

Course: Data Analytics With R

The trainer was fantastic and really knew his stuff. I learned a lot about the software I didn't know previously which will help a lot at my job!

Steve McPhail - Alberta Health Services - Information Technology

Course: Data Analysis with Hive/HiveQL

The high level principles about Hive, HDFS..

Geert Suys - Proximus Group

Course: Data Analysis with Hive/HiveQL

The handson. The mix practice/theroy

Proximus Group

Course: Data Analysis with Hive/HiveQL

Fulvio was able to grasp our companies business case and was able to correlate with the course material, almost instantly.

Samuel Peeters - Proximus Group

Course: Data Analysis with Hive/HiveQL

The trainer was excellent, He was always ready to answer my questions and share as much knowledge as he could.

Fahad Malalla - Tatweer Petroleum

Course: Advanced Python

The trainer gave the trainees opportunity to explore the application of stats analysis in their own profession and attempted to give comprehensive insight to the usefulness of different statistical methods.

Carl Dillon - Liverpool Mutual Homes

Course: Excel For Statistical Data Analysis

Translated by

good interaction with the trainer, dynamic exchange of knowledge

NIIT Limited

Course: Data Analytics With R

Translated by

Exercises on functions!

WIktor Paprzycki

Course: SQL Advanced level for Analysts

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way of translation

Małgorzata Mieczkowska

Course: SQL Advanced level for Analysts

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exercises

Wiktor Paprzycki

Course: SQL Advanced level for Analysts

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emphasis on examples with encoding "on the projector" is definitely on + for Tom.

ADVA OPTICAL NETWORKING SP. ZO O.

Course: Advanced Python

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Geo-Spatial Analysis Subcategories

Geospatial Analysis Course Outlines

Magellan is an open-source distributed execution engine for geospatial analytics on big data. Implemented on top of Apache Spark, it extends Spark SQL and provides a relational abstraction for geospatial analytics.

This instructor-led, live training introduces the concepts and approaches for implementing geospacial analytics and walks participants through the creation of a predictive analysis application using Magellan on Spark.

deck.gl is an open-source, WebGL-powered library for exploring and visualizing data assets at scale. Created by Uber, it is especially useful for gaining insights from geospatial data sources, such as data on maps.

This instructor-led, live training introduces the concepts and functionality behind deck.gl and walks participants through the set up of a demonstration project.

By the end of this training, participants will be able to:

- Take data from very large collections and turn it into compelling visual representations- Visualize data collected from transportation and journey-related use cases, such as pick-up and drop-off experiences, network traffic, etc.- Apply layering techniques to geospatial data to depict changes in data over time- Integrate deck.gl with React (for Reactive programming) and Mapbox GL (for visualizations on Mapbox based maps).- Understand and explore other use cases for deck.gl, including visualizing points collected from a 3D indoor scan, visualizing machine learning models in order to optimize their algorithms, etc.

Audience

- Developers- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice

A geographic information system (GIS) is a system designed to capture, store, manipulate, analyze, manage, and present spatial or geographic data. The acronym GIS is sometimes used for geographic information science (GIScience) to refer to the academic discipline that studies geographic information systems and is a large domain within the broader academic discipline of geoinformatics.

The use of Python with GIS has substantially increased over the last two decades, particularly with the introduction of Python 2.0 series in 2000, which included many new programming features that made the language much easier to deploy. Since that time, Python has not only been utilized within commercial GIS such as products by Esri but also open source platforms, including as part of QGIS and GRASS. In fact, Python today is by far the most widely used language by GIS users and programmers.

This program covers the usage of Python and its advance libraries like geopandas, pysal, bokeh and osmnx to implement your own GIS features. The program also covers introductory modules around ArcGIS API, and QGIS toolboox.

A geographic information system (GIS) is a system designed to capture, store, manipulate, analyze, manage, and present spatial or geographic data. The acronym GIS is sometimes used for geographic information science (GIScience) to refer to the academic discipline that studies geographic information systems and is a large domain within the broader academic discipline of geoinformatics.

QGIS functions as geographic information system (GIS) software, allowing users to analyze and edit spatial information, in addition to composing and exporting graphical maps. QGIS supports both raster and vector layers; vector data is stored as either point, line, or polygon features. Multiple formats of raster images are supported, and the software can georeference images. To summarize it allows the users to Create, edit, visualise, analyse and publish geospatial information on Windows, Mac, Linux, BSD.

This program, in its first phase, introduces the QGIS interface for general usage. In the second phase, we introduce PyQGIS - the python libraries of QGIS that allows the integration of GIS functionalities in your python code or your python application, so that you may even create your own Python Plugin around a particular GIS functionality.